Lung Segmentation in Feature Images with Gray and Shape Information

2014 ◽  
Vol 513-517 ◽  
pp. 3069-3072 ◽  
Author(s):  
Guo Dong Zhang ◽  
Yi Fei Guo ◽  
Su Gao ◽  
Wei Guo
Keyword(s):  
Segmentation Method ◽  
Shape Information ◽  
Initial Shape ◽  
Shape Model ◽  
Lung Segmentation ◽  
Computer Aided ◽  
Shape Restriction ◽  
The Mean ◽  
Overlap Rate ◽  
Aided Diagnosis

Accurate lung segmentation in chest radiography is an important and difficult task in the development of computer-aided diagnosis. Therefore, we proposed a lung segmentation method in feature images with gray and shape information. Firstly, we extracted six feature images, and built an initial shape model. Then, we calculated the gray cost in the feature images. Finally, the lung profile was determined by use of shape restriction. With the feature images and method of shape restriction, the mean overlap rate was improved to 75.60%. Therefore, the method proposed in our study can improve the performance of lung segmentation.

scholarly journals Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Gurman Gill ◽  
Reinhard R. Beichel
Keyword(s):  
Image Data ◽  
Active Shape Model ◽  
Data Sets ◽  
Segmentation Method ◽  
Data Set ◽  
Shape Model ◽  
Lung Segmentation ◽  
4D Ct ◽  
Active Shape ◽  
Ct Data

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of0.9773±0.0254, which was statistically significantly better (pvalue≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.

Statistical Shape Model of the Liver and Its Application to Computer-Aided Diagnosis of Liver Cirrhosis

2014 ◽  
Vol 190 (4) ◽  
pp. 37-45 ◽  
Author(s):  
Mei Uetani ◽  
Tomoko Tateyama ◽  
Shinya Kohara ◽  
Hidetoshi Tanaka ◽  
Xian-Hua Han ◽  
...  
Keyword(s):  
Liver Cirrhosis ◽  
Computer Aided Diagnosis ◽  
Statistical Shape Model ◽  
Shape Model ◽  
Computer Aided ◽  
Statistical Shape ◽  
Aided Diagnosis

Techniques for the Automated Segmentation of Lung in Thoracic Computed Tomography Scans

2013 ◽  
pp. 675-687
Author(s):  
William F. Sensakovic ◽  
Samuel G. Armato
Keyword(s):  
Computed Tomography ◽  
Image Data ◽  
Segmentation Method ◽  
Correlation Filters ◽  
Lung Segmentation ◽  
Thoracic Diseases ◽  
Thoracic Computed Tomography ◽  
Segmentation Accuracy ◽  
Computed Tomography Scans ◽  
Computer Aided

Computed Tomography (CT) is widely used to diagnose and assess thoracic diseases. The improved resolution of CT studies has resulted in a substantial increase of image data for analysis by radiologists. The time-consuming nature of this analysis motivates the application of Computer-Aided Diagnostic (CAD) methods to assist radiologists. Most CAD methods require identification of the lung within the patient images, a preprocessing step known as “lung segmentation.” This chapter describes an intensity-based lung segmentation method. The segmentation method begins with simple thresholding, and several image processing modules are included to improve segmentation accuracy and robustness. Common segmentation difficulties are discussed and motivate the inclusion of each module in the lung segmentation method. These modules will include brief explanations of common techniques (e.g., morphological operators) in addition to novel techniques developed specifically for lung segmentation (e.g., gradient correlation filters).

Statistical Shape Model of the Liver and Its Application to Computer Aided Diagnosis of Liver Cirrhosis

2013 ◽  
Vol 133 (11) ◽  
pp. 2037-2043
Author(s):  
Mei Uetani ◽  
Tomoko Tateyama ◽  
Shinya Kohara ◽  
Hidetoshi Tanaka ◽  
Xian-hua Han ◽  
...  
Keyword(s):  
Liver Cirrhosis ◽  
Computer Aided Diagnosis ◽  
Statistical Shape Model ◽  
Shape Model ◽  
Computer Aided ◽  
Statistical Shape ◽  
Aided Diagnosis

scholarly journals Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Gurman Gill ◽  
Matthew Toews ◽  
Reinhard R. Beichel
Keyword(s):  
A Priori ◽  
Ct Scans ◽  
Shape Information ◽  
Shape Model ◽  
Lung Segmentation ◽  
Active Shape Models ◽  
Novel Approach ◽  
Segmentation Methods ◽  
Active Shape ◽  
Feature Based

Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation.

scholarly journals An effective approach for CT lung segmentation using region growing

2021 ◽  
Vol 2082 (1) ◽  
pp. 012001
Author(s):  
Xi Yang ◽  
Guanyu Xu ◽  
Teng Zhou
Keyword(s):  
Medical Image ◽  
Lung Diseases ◽  
Region Growing ◽  
Computer Aided Diagnosis ◽  
Medical Image Segmentation ◽  
Point Selection ◽  
Seed Point ◽  
Lung Segmentation ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract X-ray is an important means of detecting lung diseases. With the increasing incidence of lung diseases, computer-aided diagnosis technology is of great significance in clinical treatment. It has become a hot research direction to use computer-aided diagnosis to recognize chest radiography images, which can alleviate the uneven status of regional medical level. For clinical diagnosis, medical image segmentation can enable users to timely obtain the target region they are interested in and analyze it, which is significant to be used as an important basis for auxiliary research and judgment. In this case, a region growing algorithm based on threshold presegmentation is selected for lung segmentation, which integrates image enhancement, threshold segmentation, seed point selection and morphological post-processing, etc., to improve the segmentation effect, which also has certain reference value for other medical image processing.

Techniques for the Automated Segmentation of Lung in Thoracic Computed Tomography Scans

2012 ◽  
pp. 145-158
Author(s):  
William F. Sensakovic ◽  
Samuel G. Armato
Keyword(s):  
Computed Tomography ◽  
Image Data ◽  
Segmentation Method ◽  
Correlation Filters ◽  
Lung Segmentation ◽  
Thoracic Diseases ◽  
Thoracic Computed Tomography ◽  
Segmentation Accuracy ◽  
Computed Tomography Scans ◽  
Computer Aided

Computed Tomography (CT) is widely used to diagnose and assess thoracic diseases. The improved resolution of CT studies has resulted in a substantial increase of image data for analysis by radiologists. The time-consuming nature of this analysis motivates the application of Computer-Aided Diagnostic (CAD) methods to assist radiologists. Most CAD methods require identification of the lung within the patient images, a preprocessing step known as “lung segmentation.” This chapter describes an intensity-based lung segmentation method. The segmentation method begins with simple thresholding, and several image processing modules are included to improve segmentation accuracy and robustness. Common segmentation difficulties are discussed and motivate the inclusion of each module in the lung segmentation method. These modules will include brief explanations of common techniques (e.g., morphological operators) in addition to novel techniques developed specifically for lung segmentation (e.g., gradient correlation filters).

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